Analysis of the impact of Customer Value Management (CVM) on increasing Cellular Packet Telkomsel (Study case: PT Telkomsel)

https://doi.org/10.35912/ijfam.v7i3.3720
Published: Dec 12, 2025

Abstract:

Purpose: This study aims to analyze the impact of the Customer Value Management (CVM) program supported by machine learning on increasing customer purchases of Telkomsel cellular data packages, as well as identifying the key behavioral factors influencing purchasing decision.

Methodology/approach: This research employs a quantitative explanatory approach using big data analytics. The dataset consists of customer transaction records over a three-month period (January–March 2023), involving 5.7 million customer data points. A supervised machine learning classification model was developed using the CatBoost Gradient Boosting Decision Tree (GBDT) algorithm to predict customer purchasing propensity, supported by Focus Group Discussions (FGD) with subject-matter experts.

Results/findings: The CatBoost model achieved an accuracy of 86% in predicting potential lapsers. The test-and-learn campaign based on CVM personalization resulted in a 6.55% increase in take-up rate and generated a revenue uplift of IDR 141.6 million. The most significant factors influencing purchases were monthly data package revenue, frequency of data usage within specific price ranges, and total monthly data revenue.

Conclusion: The findings confirm that CVM implementation supported by machine learning effectively enhances personalized marketing, improves customer targeting, and increases purchasing performance at PT Telkomsel.

Limitations: This study is limited to a single company, a three-month observation period, and the use of one machine learning algorithm.

Contribution: This study contributes empirical evidence on the effectiveness of integrating CVM and CatBoost-based machine learning in large-scale telecom marketing to optimize customer value and revenue growth.

Keywords:
1. Behavior
2. Big Data
3. Customer Value Management
4. Machine Learning
5. Propensity
Authors:
1 . Indra Syahputra
Universitas Indonesia, Depok, Jawa Barat, Indonesia
2 . Agung Nugroho
Universitas Indonesia, Depok, Jawa Barat, Indonesia
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How to Cite
Syahputra, I., & Nugroho, A. (2025). Analysis of the impact of Customer Value Management (CVM) on increasing Cellular Packet Telkomsel (Study case: PT Telkomsel). International Journal of Financial, Accounting, and Management, 7(3), 519–537. https://doi.org/10.35912/ijfam.v7i3.3720
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Issue & Section
Vol. 7 No. 3 (2025): December
Articles
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References

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  2. Atif, M. B. A. A., Shakir, A. H., Nussairi, A. K. J. A., Mohammed, J., & Almusawi, A. S. A. (2022). Data Mining with its Role in Marketing, Sales Support and Customer Identification Data Analysis. International Journal Artificial Intelligent and Informatics, 3(2), 104-116. doi:https://doi.org/10.33292/ijarlit.v3i2.52
  3. Bisjoe, H., & Rizal, A. (2018). Menjaring Data dan Informasi Penelitian melalui FGD (Focus Group Discussion): Belajar dari Praktik Lapang. Jurnal Penelitian Sosial dan Ekonomi Kehutanan, 15(1), 17-27. doi:https://doi.org/10.20886/buleboni.2018.v15.pp17-27
  4. Bleier, A., & Eisenbeiss, M. (2015). Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where. Marketing Science, 34(5), 669-688. doi:https://doi.org/10.1287/mksc.2015.0930
  5. Boateng, H. (2016). Customer Knowledge Management Practices on a Social Media Platform: A Case Study of MTN Ghana and Vodafone Ghana. Information Development, 32(3), 440-451. doi:https://doi.org/10.1177/0266666914554723
  6. Boch, M., Gindl, S., Barnett, A., Margetis, G., Mireles, V., Adamakis, E., & Knoth, P. (2022). A Systematic Review of Data Management Platforms. World Conference on Information Systems and Technologies, 15-24. doi:https://doi.org/10.1007/978-3-031-04819-7_2
  7. Efita, W. (2023). Pengaruh Nilai Pelanggan dan Kepuasan Pelanggan Terhadap Loyalitas Pelanggan pada Servis Kendaraan di PT. Agung Automall Harapan Raya Pekanbaru. Jurnal Ekonomi Bisnis dan Manajemen, 1(1), 34-44. doi:https://doi.org/10.59024/jise.v1i1.66
  8. Eggert, A., Ulaga, W., Frow, P., & Payne, A. (2018). Conceptualizing and Communicating Value in Business Markets: From Value in Exchange to Value in Use. Industrial Marketing Management, 69, 80-90. doi:https://doi.org/10.1016/j.indmarman.2018.01.018
  9. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 69(2), 897-904. doi:https://doi.org/10.1016/j.jbusres.2015.07.001
  10. Homburg, C., Jozi?, D., & Kuehnl, C. (2017). Customer Experience Management: Toward Implementing an Evolving Marketing Concept. Journal of the academy of marketing science, 45(3), 377-401. doi:https://doi.org/10.1007/s11747-015-0460-7
  11. Ibrahim, M. M., Ariyanti, M., & Iskamto, D. (2025). The Effect of Sales Promotion and Hedonic Shopping Motivation on Impulse Buying Behavior and the Impact to Customer Loyalty in Social Commerce TikTok Shop. Global Academy of Business Studies, 1(4), 281-296. doi:https://doi.org/10.35912/gabs.v1i4.3467
  12. Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Journal of marketing, 80(6), 36-68. doi:https://doi.org/10.1509/jm.15.0414
  13. Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of marketing, 80(6), 69-96. doi:https://doi.org/10.1509/jm.15.0420
  14. Padual, S. R. M., Ong, A. K. S., German, J. D., & Gumasing, M. J. J. (2024). The Need for Individualization: An Open Innovation Perspective on the Case for Customized Products. Acta Psychologica, 249, 1-14. doi:https://doi.org/10.1016/j.actpsy.2024.104473
  15. Payne, A., Frow, P., & Eggert, A. (2017). The Customer Value Proposition: Evolution, Development, and Application in Marketing. Journal of the academy of marketing science, 45(4), 467-489. doi:https://doi.org/10.1007/s11747-017-0523-z
  16. Penta, M. D. (2016). Combining Quantitative and Qualitative Methods (When Mining Software Data). Perspectives on Data Science for Software Engineering, 205-211. doi:https://doi.org/10.1016/B978-0-12-804206-9.00038-6
  17. Purwanti, A. E., & Lupiana, F. (2023). Peran Sistem Informasi Pemasaran dalam Mengelola Proses Pemasaran Melalui Digital Marketing. Jurnal Ilmiah Manajemen, Ekonomi Dan Bisnis, 2(2), 88-102. doi:https://doi.org/10.51903/jimeb.v2i2.607
  18. Rachman, M. A., & Ariyanti, M. (2025). The Impact of Customer Relationship Management on Customer Loyalty with Customer Satisfaction as a Mediating Variable at Telkomsel Partner Outlets in Banjar City. International Journal of Accounting and Management Information Systems, 3(1), 1-13. doi:https://doi.org/10.35912/ijamis.v3i1.3242
  19. Rosário, A. T., & Dias, J. C. (2023). How has Data-Driven Marketing Evolved: Challenges and Opportunities with Emerging Technologies. International Journal of Information Management Data Insights, 3(2), 1-14. doi:https://doi.org/10.1016/j.jjimei.2023.100203
  20. Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 1-16. doi:https://doi.org/10.1016/j.ssresearch.2022.102817
  21. Stremersch, S., Cabooter, E., Guitart, I. A., & Camacho, N. (2025). Customer Insights for Innovation: A Framework and Research Agenda for Marketing. Journal of the academy of marketing science, 53(1), 29-51. doi:https://doi.org/10.1007/s11747-024-01051-8
  22. Sugiyono. (2017). Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Alfabeta.
  23. Suka, E. A. A. G., & Lubis, S. (2019). Strategi Komunikasi Pemasaran dalam Mempromosikan Produk Homemade Kefir di Kota Medan. Jurnal Ilmu Komunikasi Network Media, 2(2), 1-27. doi:https://doi.org/10.46576/jnm.v2i2.562
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